Sparse learning methods have been powerful tools for learning compact representations of functional brain networks consisting of a set of brain network nodes and a connectivity matrix measuring functional coherence between the nodes. However, these tools typically focus on the functional connectivity measures alone, ignoring the brain network nodal information that is complementary to the functional connectivity measures for comprehensively characterizing the functional brain networks. In order to provide a comprehensive delineation of the functional brain networks, we develop a new data fusion method for heterogeneous data, aiming at learning sparse network patterns to characterize both the functional connectivity measures and their complementary network nodal information within a unified framework. Experimental results have demonstrated that our method outperforms the best alternative method under comparison in terms of accuracy on simulated data as well as both reproducibility and prediction performance of brain age on real resting state functional magnetic resonance imaging data.

稀疏学习方法已成为用于学习功能性大脑网络的紧凑表示的强大工具，该功能性大脑网络由一组大脑网络节点和测量节点之间功能一致性的连通性矩阵组成。但是，这些工具通常只关注功能连接性度量，而忽略了脑网络节点信息，该信息是对功能性连接度量进行全面表征的功能性网络的补充。为了提供功能性大脑网络的全面描述，我们针对异类数据开发了一种新的数据融合方法，旨在学习稀疏网络模式，以在统一框架内表征功能连接性度量及其互补网络节点信息。